1. Field of Invention
The present invention relates the field of magnetocardiogram (MCG) imaging. More specifically, it relates to the generation of high-resolution MCG images from sparse data input from an electromagnetic sensor unit.
2. Description of Related Art
Cardiac electric currents are generated by electrophysiological processes in the heart. Localizing abnormal electric currents is very important for diagnosing ischemic diseases such as myocardial infarction, angina cordis, etc. It also benefits patients in the catheter lab for both treatment and follow-up, as is explained in “Forty Years of Magnetocardiology”, by F. Stroink, in Int. Conf. on Biomagnetism Advances in Biomagnetism, 28:1-8, 2010.
Traditionally, cardiac electric activities such as arrhythmia are diagnosed by means of an electrocardiogram (ECG). However, since an ECG only provides temporal information, it cannot localize abnormal currents in the heart directly, even if the ischemic disease has been detected. However, by using a large number of electrodes (leads), Body Surface Potential Mapping (BSPM) is able to reconstruct a body surface potential map, as is explained in “Noninvasive volumetric imaging of cardiac electrophysiology”, by Wang et al., in CVPR, pages 2176-2183, 2009. Nonetheless, the accuracy of electric current localization is still limited because the signals are often distorted due to the poor conductivity of body tissue.
The advent of the magnetocardiogram, or magnetocardiography, (MCG) provides more accurate measurements of cardiac electric currents, both spatially and temporally. With reference to FIG. 1A, an MCG system consists of a sensor unit 11 consisting of a small number of electromagnetic sensors 13 (typically arranged as a planar array of sixty-four or fewer sensors). Electrical impulses within the body create a magnetic field 15. In the present case, the human heart 19 functions as the observed current source 17.
Each sensor 13 is a capture point, and hereinafter may be referenced as a capture 13. Each capture 13 measures a one-dimensional (i.e. 1D) magnetic waveform in a direction perpendicular to the sensor planar array (i.e. the z-direction) emanating from the patient's chest 21 (i.e. human torso). The MCG sensor unit 11 is usually placed five to ten centimeters above the patient's chest 21, and measures the patient's heart magnetic field in a non-invasive way. At each capture 13 a low resolution (hereinafter, low-res), two-dimensional (2D) MCG map of electromagnetic activity is measured.
Compared to ECG, MCG has a few advantages. First, the magnetic field generated by the heart's electrical impulses (i.e. currents) is not distorted in the direction perpendicular to the body surface (i.e., z direction), due to the magnetic property of body tissue. Thus MCG is more accurate and sensitive to weak electrical activities in the early stage of heart disorders. Second, the MCG sensor array can localize the position of electrical currents in the heart. Finally, MCG measurements are non-invasive. After forty years of research in MCG, cardiac electric current localization and high resolution visualization for MCG measurements are attracting more and more interest from both research and clinical areas.
However, there are a number of difficulties associated with MCG, which so far has prevented MCG from becoming a mainstream medical diagnostic tool in cardiology. One difficulty is that the low-res 2D MCG maps are not sufficient for localizing electric currents in the heart. For example, a 64 channel Hitachi™ MCG system with a 25 mm sensor interval (as described in “Newly Developed Magnetocardiographic System for Diagnosing Heart Disease”, by Tsukada et al., in Hitachi Review, 50(1):13-17, 2001) only measures an 8×8 MCG map (i.e. an 8×8 array of 64 measurement points).
The resolution of an MCG map is limited due to the size of the sensors 13, which limit the number of sensors 13 that an MCG sensor unit 11 may have.
Thus, a necessary step in MCG, is creating a high resolution (hereinafter High-res) MCG image, or map, from a low-res 2D MCG map. Two image examples 23 and 25 of such high-res images are shown in FIG. 1B. Image 23 shows the tangential image of a restored high-res MCG image of a healthy heart. The maximal point 25 (i.e. strongest point) within image 23 indicates the location (or source) of electric current in the heart. Thus, high-res MCG images permits doctors to directly “see” the electrical activity in the heart. Image 25 shows the tangential image of a restored high-res MCG image of an unhealthy heart. It differs significantly from image 23 of a healthy heart, and thus provides important cues for diagnosis. Compared to low-res MCG maps, high-res MCG images provide more diagnostic significance, and serve as the basis for an accurate electric current localization.
Most current MCG systems use curve fitting interpolation methods to reconstruct high-res MCG images from low-res 2D MCG maps, as is shown in “Magnetocardiographic Localization of Arrhythmia Substrates: A Methodology Study With Accessory Path-Way Ablation as Reference”, by B. A. S. et al., in Ann Noninvasive Electrocardiol, 10(2):152-160, 2005, and shown in “Evaluation of an Infarction Vector by Magnetocardiogram: Detection of Electromotive Forces that Cannot be Deduced from an Electrocardiogram”, by Nomura et al, in Int. Congress Series, 1300:512-515, 2007. Unfortunately, the accuracy of curve fitting methods is usually limited.
Another method for improving the accuracy of high-res MCG images first reconstructs a three-dimensional (3D) position, magnitude and orientation of electric currents, given the low-res MCG measurements. This method is generally called the inverse problem, and is more fully explained in “Magnetocardiographic Localization of Arrhythmia Substrates: A Methodology Study with Accessory Pathway Ablation as Reference”, by R. J. et al., in Europace, 11(2):169-177, 2009, and explained in “Conversion of Magnetocardiographic Recordings Between Two Different Multichannel Squid Devices”, by M. B. et al., in IEEE Trans. on Biomedical Engineering, 47(7):869-875, 2000. This method generally computes a high-res MCG image based on current reconstructed by the Biot-Savart law.
As it is known in the art, however, according to the Helmboltz reciprocity principal, the inverse problem for MCG is an ill posed problem unless the number of electric currents is known. But even when the current number is known, it requires solving a large scale nonlinear optimization problem which is often computationally expensive and may lead to undesired local minimum.
R. J. et al. therefore propose a simplified solution by assuming a single electric current located at the world origin and far from the sensor array. As it is known in the art, linear solutions may be presented for special cases where the current positions are fixed at uniform grids in the heart. The presented linear solutions can be over-constrained or under-constrained. These methods, however, make another assumption that the sensor array and heart are perfectly aligned. In practice, these assumptions often can be difficult to satisfy.
Therefore, high-res MCG image restoration based on these types of methods can often be unreliable.
Recently machine learning techniques have been applied to high-res MCG image restoration. An example of this approach applies learned nonlinear interpolation functions using neural networks.